EDA

Numeric Demographic and Clinical Characteristics
Variable Description Mean SD Median Min Max
Age Age in years 40.14 11.21 39.00 18.00 70.00
Height Height in cm 169.70 9.38 170.18 100.00 198.12
Weight Weight in pounds 169.56 49.20 165.00 30.00 350.00
Years in Canada Duration living in Canada (years) 15.62 20.94 0.00 0.00 70.00
Years in US Duration living in United States (years) 15.48 18.67 0.00 0.00 59.00
Current Work Ability (0-10) Self-rated current work ability (0 = cannot work, 10 = best) 7.38 1.93 8.00 1.00 10.00
OHQ Score Oxford Happiness Questionnaire score 3.60 0.76 3.53 1.83 5.41
WHOQOL Score WHOQOL-BREF score 3.52 0.69 3.59 1.73 5.00
Negative Feelings Frequency of Negative Feelings 2.84 0.97 3.00 1.00 5.00

## # A tibble: 4 × 6
##   country         Age_Mean Height_Mean Weight_Mean Years_in_Canada Years_in_US
##   <chr>              <dbl>       <dbl>       <dbl>           <dbl>       <dbl>
## 1 ""                  31          167.        194.          11.7         9    
## 2 "Canada"            43.0        170.        174.          39.6         0.192
## 3 "Other"             43.6        171.        164.          10.8         3.10 
## 4 "United States"     36.9        170.        167.           0.851      32.4
##    work_hours    overtime_hours  
##  Min.   :42.00   Min.   : 0.000  
##  1st Qu.:48.00   1st Qu.: 0.000  
##  Median :50.00   Median : 1.000  
##  Mean   :53.48   Mean   : 6.273  
##  3rd Qu.:60.00   3rd Qu.: 8.000  
##  Max.   :72.00   Max.   :75.000  
##  NA's   :179     NA's   :7
## # A tibble: 4 × 5
##   country         work_hours_mean work_hours_sd overtime_mean overtime_sd
##   <chr>                     <dbl>         <dbl>         <dbl>       <dbl>
## 1 ""                         55.5         10.6          14          18.4 
## 2 "Canada"                   56.2          9.56          3.77        9.61
## 3 "Other"                    51            5.23          4.31        6.40
## 4 "United States"            53            9.19          8.64       13.3

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    0.00    3.00    5.00    7.75   10.00   48.00      14
## # A tibble: 2 × 8
##   country       tenure_mean tenure_sd   min    q1 median    q3   max
##   <chr>               <dbl>     <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl>
## 1 Canada               8.60      8.14     0  1.75      5    15    26
## 2 United States        7.09      7.43     0  3         5     9    48

Happiness and QoL EDA

  OHQ Score
Predictors Estimates CI p
(Intercept) 2.51 -2.34 – 7.36 0.296
work hours 0.03 -0.06 – 0.11 0.511
country [United States] 1.60 -3.80 – 7.01 0.545
work hours × country
[United States]
-0.04 -0.13 – 0.06 0.450
Observations 27
R2 / R2 adjusted 0.059 / -0.064

  WHOQOL Score
Predictors Estimates CI p
(Intercept) 3.61 -0.05 – 7.26 0.053
work hours 0.00 -0.06 – 0.07 0.949
country [United States] 0.50 -3.58 – 4.57 0.802
work hours × country
[United States]
-0.01 -0.08 – 0.06 0.736
Observations 27
R2 / R2 adjusted 0.025 / -0.102
## Analysis of Variance Table
## 
## Model 1: WHOQOL_Score ~ work_hours + country
## Model 2: WHOQOL_Score ~ work_hours * country
##   Res.Df    RSS Df Sum of Sq     F Pr(>F)
## 1     24 10.220                          
## 2     23 10.168  1  0.051306 0.116 0.7365

## Conditional item response (column) probabilities,
##  by outcome variable, for each class (row) 
##  
## $OHQ_Score
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.3279 0.0000 0.6721
## class 2:  0.2023 0.7977 0.0000
## class 3:  0.0000 0.0000 1.0000
## 
## $WHOQOL_Score
##            Pr(1)  Pr(2) Pr(3)
## class 1:  0.3279 0.6721     0
## class 2:  0.3619 0.6381     0
## class 3:  0.0000 0.0000     1
## 
## $work_hours
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.0000 0.6721 0.3279
## class 2:  0.7977 0.0000 0.2023
## class 3:  0.7000 0.2000 0.1000
## 
## $overtime_hours
##            Pr(1)  Pr(2) Pr(3)
## class 1:  0.7760 0.0000 0.224
## class 2:  0.7607 0.2393 0.000
## class 3:  1.0000 0.0000 0.000
## 
## Estimated class population shares 
##  0.1653 0.4643 0.3704 
##  
## Predicted class memberships (by modal posterior prob.) 
##  0.1481 0.4815 0.3704 
##  
## ========================================================= 
## Fit for 3 latent classes: 
## ========================================================= 
## number of observations: 27 
## number of estimated parameters: 26 
## residual degrees of freedom: 1 
## maximum log-likelihood: -73.48876 
##  
## AIC(3): 198.9775
## BIC(3): 232.6693
## G^2(3): 28.83653 (Likelihood ratio/deviance statistic) 
## X^2(3): 32.91627 (Chi-square goodness of fit) 
## 
## # A tibble: 4 × 4
##   lca_class   age work_hours OHQ_Score
##       <int> <dbl>      <dbl>     <dbl>
## 1         1  41.5       62        3.54
## 2         2  39.9       52.2      3.20
## 3         3  37.1       52.3      4.57
## 4        NA  39.7      NaN        3.61
## 
## Call:
## glm(formula = eap_used ~ OHQ_Score + WHOQOL_Score + work_hours + 
##     overtime_hours + gender + age + income, family = binomial, 
##     data = telus)
## 
## Coefficients: (1 not defined because of singularities)
##                          Estimate Std. Error z value Pr(>|z|)
## (Intercept)            -2.410e+02  1.561e+06   0.000        1
## OHQ_Score               5.598e+01  4.215e+05   0.000        1
## WHOQOL_Score           -6.739e+01  9.635e+05   0.000        1
## work_hours              5.059e+00  9.691e+03   0.001        1
## overtime_hours         -7.041e+00  1.190e+04  -0.001        1
## genderMale              6.575e+01  1.318e+05   0.000        1
## genderTransgender Male -1.043e+02  1.009e+06   0.000        1
## age                    -2.405e-01  9.005e+03   0.000        1
## income$30,000-$39,999   2.005e+01  2.894e+05   0.000        1
## income$40,000-$49,999   1.784e+02  4.745e+07   0.000        1
## income$50,000-$59,999   2.848e+01  5.108e+05   0.000        1
## income$60,000-$69,999   2.479e+01  4.813e+05   0.000        1
## income$70,000-$79,999   1.184e+01  2.644e+05   0.000        1
## income$80,000 or more          NA         NA      NA       NA
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2.2325e+01  on 25  degrees of freedom
## Residual deviance: 9.5977e-10  on 13  degrees of freedom
##   (153 observations deleted due to missingness)
## AIC: 26
## 
## Number of Fisher Scoring iterations: 25
##            (Intercept)              OHQ_Score           WHOQOL_Score 
##           0.000000e+00           2.055083e+24           0.000000e+00 
##             work_hours         overtime_hours             genderMale 
##           1.574500e+02           0.000000e+00           3.600270e+28 
## genderTransgender Male                    age  income$30,000-$39,999 
##           0.000000e+00           7.900000e-01           5.085040e+08 
##  income$40,000-$49,999  income$50,000-$59,999  income$60,000-$69,999 
##           2.941558e+77           2.335478e+12           5.810071e+10 
##  income$70,000-$79,999  income$80,000 or more 
##           1.381442e+05                     NA

## ---------------------------------------------------- 
## Gaussian finite mixture model fitted by EM algorithm 
## ---------------------------------------------------- 
## 
## Mclust VEV (ellipsoidal, equal shape) model with 3 components: 
## 
##  log-likelihood  n df       BIC       ICL
##       -89.54785 26 54 -355.0329 -355.0518
## 
## Clustering table:
##  1  2  3 
##  5 13  8

## ---------------------------------------------------- 
## Gaussian finite mixture model fitted by EM algorithm 
## ---------------------------------------------------- 
## 
## Mclust EII (spherical, equal volume) model with 5 components: 
## 
##  log-likelihood   n  df       BIC       ICL
##       -5298.208 150 150 -11348.01 -11353.77
## 
## Clustering table:
##  1  2  3  4  5 
## 24 16 45  6 59

## Conditional item response (column) probabilities,
##  by outcome variable, for each class (row) 
##  
## $I.don.t.feel.particularly.pleased.with.the.way.I.am
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.2450 0.2308 0.5242
## class 2:  0.2859 0.5181 0.1961
## class 3:  0.6552 0.2418 0.1030
## 
## $I.am.intensely.interested.in.other.people
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.1238 0.2923 0.5839
## class 2:  0.2310 0.5178 0.2511
## class 3:  0.3786 0.3797 0.2417
## 
## $I.feel.that.life.is.very.rewarding
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.0000 0.0776 0.9224
## class 2:  0.0542 0.6425 0.3033
## class 3:  0.2732 0.5500 0.1768
## 
## $I.have.very.warm.feelings.towards.almost.everyone
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.0771 0.1697 0.7532
## class 2:  0.1592 0.5900 0.2508
## class 3:  0.3116 0.5486 0.1398
## 
## $I.rarely.wake.up.feeling.rested
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.3071 0.2772 0.4157
## class 2:  0.3208 0.4645 0.2147
## class 3:  0.5528 0.2070 0.2403
## 
## $I.am.not.particularly.optimistic.about.the.future
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.1847 0.1685 0.6468
## class 2:  0.1962 0.5544 0.2495
## class 3:  0.5842 0.2424 0.1734
## 
## $I.find.most.things.amusing
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.0460 0.2763 0.6777
## class 2:  0.0716 0.6250 0.3035
## class 3:  0.4117 0.4500 0.1384
## 
## $I.am.always.committed.and.involved
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.0155 0.1847 0.7998
## class 2:  0.0527 0.6239 0.3234
## class 3:  0.3101 0.4158 0.2741
## 
## $Life.is.good
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.0000 0.0308 0.9692
## class 2:  0.0000 0.6237 0.3763
## class 3:  0.5141 0.3474 0.1385
## 
## $I.don.t.think.that.the.world.is.a.good.place
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.1835 0.2318 0.5847
## class 2:  0.1976 0.6952 0.1072
## class 3:  0.4813 0.2773 0.2414
## 
## $I.laugh.a.lot
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.0000 0.1848 0.8152
## class 2:  0.0529 0.6622 0.2849
## class 3:  0.3100 0.5137 0.1763
## 
## $I.am.well.satisfied.about.everything.in.my.life
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.0456 0.4154 0.5391
## class 2:  0.1441 0.6782 0.1777
## class 3:  0.6850 0.2784 0.0366
## 
## $I.don.t.think.I.look.attractive
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.2001 0.1540 0.6460
## class 2:  0.1957 0.5888 0.2156
## class 3:  0.3795 0.3117 0.3087
## 
## $There.is.a.gap.between.what.I.would.like.to.do.and.what.I.have.done
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.3081 0.5534 0.1385
## class 2:  0.3736 0.6083 0.0180
## class 3:  0.6550 0.2422 0.1028
## 
## $I.am.very.happy
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.0000 0.2462 0.7538
## class 2:  0.0000 0.8561 0.1439
## class 3:  0.7198 0.2113 0.0690
## 
## $I.find.beauty.in.some.things
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.0152 0.0615 0.9233
## class 2:  0.0000 0.5545 0.4455
## class 3:  0.1032 0.4461 0.4507
## 
## $I.always.have.a.cheerful.effect.on.others
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.0000 0.1232 0.8768
## class 2:  0.0532 0.7505 0.1963
## class 3:  0.3436 0.4474 0.2090
## 
## $I.can.fit.in..find.time.for..everything.I.want.to
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.0154 0.4454 0.5392
## class 2:  0.1773 0.6087 0.2141
## class 3:  0.5515 0.3104 0.1381
## 
## $I.feel.that.I.am.not.especially.in.control.of.my.life
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.1231 0.2303 0.6467
## class 2:  0.0703 0.7678 0.1618
## class 3:  0.4826 0.3129 0.2045
## 
## $I.feel.able.to.take.anything.on
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.0617 0.1990 0.7393
## class 2:  0.0533 0.7147 0.2320
## class 3:  0.5832 0.3129 0.1039
## 
## $I.feel.fully.mentally.alert
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.0308 0.1700 0.7992
## class 2:  0.0694 0.6792 0.2514
## class 3:  0.4154 0.4798 0.1048
## 
## $I.often.experience.joy.and.elation
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.0306 0.2142 0.7552
## class 2:  0.0352 0.7509 0.2139
## class 3:  0.4128 0.4840 0.1032
## 
## $I.don.t.find.it.easy.to.make.decisions
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.1988 0.2471 0.5541
## class 2:  0.2147 0.6242 0.1611
## class 3:  0.3458 0.4136 0.2406
## 
## $I.don.t.have.a.particular.sense.of.meaning.and.purpose.in.my.life
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.2002 0.0757 0.7241
## class 2:  0.0903 0.6090 0.3007
## class 3:  0.4097 0.3786 0.2117
## 
## $I.feel.I.have.a.great.deal.of.energy
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.0776 0.2312 0.6912
## class 2:  0.1426 0.7309 0.1265
## class 3:  0.6508 0.2104 0.1388
## 
## $I.usually.have.a.good.influence.on.events
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.0154 0.1377 0.8469
## class 2:  0.0000 0.8219 0.1781
## class 3:  0.3427 0.5182 0.1390
## 
## $I.don.t.have.fun.with.other.people
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.1371 0.0616 0.8013
## class 2:  0.0726 0.6430 0.2844
## class 3:  0.1384 0.5504 0.3112
## 
## $I.don.t.feel.particularly.healthy
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.1533 0.1232 0.7235
## class 2:  0.2125 0.5370 0.2505
## class 3:  0.2116 0.6506 0.1379
## 
## $I.don.t.have.particularly.happy.memories.of.the.past
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.1371 0.1232 0.7396
## class 2:  0.1977 0.4805 0.3218
## class 3:  0.1386 0.4847 0.3766
## 
## Estimated class population shares 
##  0.4329 0.3726 0.1945 
##  
## Predicted class memberships (by modal posterior prob.) 
##  0.4333 0.3733 0.1933 
##  
## ========================================================= 
## Fit for 3 latent classes: 
## ========================================================= 
## number of observations: 150 
## number of estimated parameters: 176 
## residual degrees of freedom: -26 
## maximum log-likelihood: -3638.342 
##  
## AIC(3): 7628.683
## BIC(3): 8158.555
## G^2(3): 5776.265 (Likelihood ratio/deviance statistic) 
## X^2(3): 8.059502e+15 (Chi-square goodness of fit) 
##  
## ALERT: number of parameters estimated ( 176 ) exceeds number of observations ( 150 ) 
##  
## ALERT: negative degrees of freedom; respecify model 
## 

##                             Df Sum Sq Mean Sq F value Pr(>F)
## factor(ohq_mclust_cluster)   4   1.66  0.4160    0.69    0.6
## Residuals                  145  87.46  0.6032               
## 29 observations deleted due to missingness
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = OHQ_Score ~ factor(ohq_mclust_cluster), data = telus)
## 
## $`factor(ohq_mclust_cluster)`
##             diff        lwr       upr     p adj
## 2-1  0.070513478 -0.6219090 0.7629360 0.9986150
## 3-1  0.088022259 -0.4542526 0.6302971 0.9915618
## 4-1 -0.459222770 -1.4384560 0.5200105 0.6944312
## 5-1 -0.003433587 -0.5228482 0.5159811 1.0000000
## 3-2  0.017508780 -0.6069524 0.6419700 0.9999918
## 4-2 -0.529736248 -1.5567648 0.4972923 0.6127173
## 5-2 -0.073947066 -0.6786630 0.5307689 0.9971679
## 4-3 -0.547245028 -1.4796616 0.3851716 0.4863009
## 5-3 -0.091455846 -0.5160668 0.3331551 0.9756491
## 5-4  0.455789182 -0.4635205 1.3750989 0.6480536
## ---------------------------------------------------- 
## Gaussian finite mixture model fitted by EM algorithm 
## ---------------------------------------------------- 
## 
## Mclust VII (spherical, varying volume) model with 4 components: 
## 
##  log-likelihood   n df       BIC       ICL
##       -4326.608 163 95 -9137.122 -9145.295
## 
## Clustering table:
##  1  2  3  4 
## 76 72 10  5

## Conditional item response (column) probabilities,
##  by outcome variable, for each class (row) 
##  
## $How.would.you.rate.your.quality.of.life.
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.0000 0.0281 0.9719
## class 2:  0.0000 0.1973 0.8027
## class 3:  0.3306 0.4979 0.1715
## 
## $How.satisfied.are.you.with.your.health.
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.0679 0.0932 0.8388
## class 2:  0.1482 0.3820 0.4698
## class 3:  0.6047 0.1704 0.2249
## 
## $To.what.extent.do.you.feel.that.physical.pain.prevents.you.from.doing.what.you.need.to.do.
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.2339 0.1003 0.6658
## class 2:  0.2765 0.2483 0.4752
## class 3:  0.3582 0.1404 0.5014
## 
## $How.much.do.you.need.any.medical.treatment.to.function.in.your.daily.life.
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.1846 0.0950 0.7204
## class 2:  0.1410 0.3118 0.5472
## class 3:  0.2763 0.1914 0.5323
## 
## $How.much.do.you.enjoy.life.
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.0000 0.0567 0.9433
## class 2:  0.0000 0.6098 0.3902
## class 3:  0.6062 0.2802 0.1136
## 
## $To.what.extent.do.you.feel.your.life.to.be.meaningful.
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.0000 0.0554 0.9446
## class 2:  0.0538 0.5764 0.3699
## class 3:  0.5516 0.2236 0.2248
## 
## $How.well.are.you.able.to.concentrate.
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.0000 0.1469 0.8531
## class 2:  0.1240 0.4402 0.4358
## class 3:  0.3342 0.5272 0.1387
## 
## $How.safe.do.you.feel.in.your.daily.life.
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.0000 0.0434 0.9566
## class 2:  0.0533 0.5017 0.4450
## class 3:  0.3043 0.3335 0.3621
## 
## $How.healthy.is.your.physical.environment.
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.0000 0.0304 0.9696
## class 2:  0.0543 0.5539 0.3917
## class 3:  0.4681 0.3346 0.1973
## 
## $Do.you.have.enough.energy.for.everyday.life.
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.0000 0.1005 0.8995
## class 2:  0.1590 0.4287 0.4123
## class 3:  0.6665 0.2226 0.1108
## 
## $Are.you.able.to.accept.your.bodily.appearance.
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.0432 0.1505 0.8063
## class 2:  0.2138 0.4709 0.3153
## class 3:  0.6358 0.2253 0.1389
## 
## $Have.you.enough.money.to.meet.your.needs.
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.1272 0.2144 0.6584
## class 2:  0.3394 0.3543 0.3062
## class 3:  0.5544 0.2778 0.1678
## 
## $How.available.to.you.is.the.information.that.you.need.in.your.day.to.day.life.
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.0000 0.0686 0.9314
## class 2:  0.0696 0.3997 0.5307
## class 3:  0.3622 0.2195 0.4183
## 
## $To.what.extent.do.you.have.the.opportunity.for.leisure.activities
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.0766 0.1679 0.7555
## class 2:  0.1507 0.5235 0.3258
## class 3:  0.5287 0.3312 0.1401
## 
## $How.well.are.you.able.to.get.around.
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.0000 0.0401 0.9599
## class 2:  0.0718 0.2875 0.6407
## class 3:  0.2210 0.2538 0.5252
## 
## $How.satisfied.are.you.with.your.sleep.
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.1215 0.1146 0.7640
## class 2:  0.3283 0.3021 0.3695
## class 3:  0.6928 0.1127 0.1944
## 
## $How.satisfied.are.you.with.your.ability.to.perform.your.daily.living.activities.
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.0133 0.0359 0.9508
## class 2:  0.0365 0.4359 0.5275
## class 3:  0.5794 0.2282 0.1924
## 
## $How.satisfied.are.you.with.your.capacity.for.work.
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.0133 0.0936 0.8931
## class 2:  0.1608 0.3279 0.5112
## class 3:  0.4723 0.1696 0.3581
## 
## $How.satisfied.are.you.with.yourself.
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.0000 0.0417 0.9583
## class 2:  0.1233 0.3432 0.5335
## class 3:  0.6658 0.2207 0.1134
## 
## $How.satisfied.are.you.with.your.personal.relationships.
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.0315 0.0586 0.9100
## class 2:  0.3531 0.3908 0.2562
## class 3:  0.5839 0.1150 0.3011
## 
## $How.satisfied.are.you.with.your.sex.life.
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.1116 0.1287 0.7597
## class 2:  0.2724 0.4602 0.2674
## class 3:  0.6324 0.0916 0.2761
## 
## $How.satisfied.are.you.with.the.support.you.get.from.your.friends.
##            Pr(1)  Pr(2)  Pr(3)
## class 1:  0.0294 0.0575 0.9131
## class 2:  0.1231 0.5167 0.3602
## class 3:  0.4983 0.2554 0.2464
## 
## Estimated class population shares 
##  0.4374 0.3399 0.2227 
##  
## Predicted class memberships (by modal posterior prob.) 
##  0.4417 0.3374 0.2209 
##  
## ========================================================= 
## Fit for 3 latent classes: 
## ========================================================= 
## number of observations: 163 
## number of estimated parameters: 134 
## residual degrees of freedom: 29 
## maximum log-likelihood: -2740.551 
##  
## AIC(3): 5749.101
## BIC(3): 6163.664
## G^2(3): 3911.686 (Likelihood ratio/deviance statistic) 
## X^2(3): 37760400525 (Chi-square goodness of fit) 
## 

##                                Df Sum Sq Mean Sq F value Pr(>F)  
## factor(whoqol_mclust_cluster)   3   4.42  1.4722   3.763 0.0121 *
## Residuals                     159  62.21  0.3912                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 16 observations deleted due to missingness
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = WHOQOL_Score ~ factor(whoqol_mclust_cluster), data = telus)
## 
## $`factor(whoqol_mclust_cluster)`
##             diff        lwr        upr     p adj
## 2-1 -0.001298876 -0.2683828  0.2657850 0.9999993
## 3-1  0.039889796 -0.5064116  0.5861912 0.9975745
## 4-1 -0.951019295 -1.7008109 -0.2012277 0.0066353
## 3-2  0.041188672 -0.5068744  0.5892518 0.9973576
## 4-2 -0.949720418 -1.7007966 -0.1986442 0.0068544
## 4-3 -0.990909091 -1.8804184 -0.1013998 0.0224092

With other vars

Categorical Analysis

Injury

## 
## Frequency Table:
## 
##                                                        No (0) 
##                              3                            131 
##           Yes, own opinion (2) Yes, physician's diagnosis (1) 
##                             16                             29 
## 
## Proportions:
## 
##                                                        No (0) 
##                     0.01675978                     0.73184358 
##           Yes, own opinion (2) Yes, physician's diagnosis (1) 
##                     0.08938547                     0.16201117 
## 
## Summary Statistics:
##          data[[categorical_var]] data[[continuous_var]].mean
## 1                                                  3.6909091
## 2                         No (0)                   3.5748486
## 3           Yes, own opinion (2)                   3.7625812
## 4 Yes, physician's diagnosis (1)                   3.2372742
##   data[[continuous_var]].sd data[[continuous_var]].n
## 1                 0.3453349                3.0000000
## 2                 0.6461397              131.0000000
## 3                 0.5161290               16.0000000
## 4                 0.6668480               29.0000000
## 
## ANOVA Results:
##                            Df Sum Sq Mean Sq F value Pr(>F)  
## Injury.due.to.an.accident   3   3.68  1.2258   3.023 0.0311 *
## Residuals                 175  70.96  0.4055                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Post-hoc Pairwise Comparisons (Tukey HSD):
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = as.formula(paste(continuous_var, "~", categorical_var)), data = data)
## 
## $Injury.due.to.an.accident
##                                                            diff        lwr
## No (0)-                                             -0.11606045 -1.0805452
## Yes, own opinion (2)-                                0.07167208 -0.9675191
## Yes, physician's diagnosis (1)-                     -0.45363487 -1.4553740
## Yes, own opinion (2)-No (0)                          0.18773253 -0.2496911
## Yes, physician's diagnosis (1)-No (0)               -0.33757442 -0.6765470
## Yes, physician's diagnosis (1)-Yes, own opinion (2) -0.52530695 -1.0396899
##                                                              upr     p adj
## No (0)-                                              0.848424339 0.9894292
## Yes, own opinion (2)-                                1.110863229 0.9979598
## Yes, physician's diagnosis (1)-                      0.548104219 0.6436492
## Yes, own opinion (2)-No (0)                          0.625156204 0.6818760
## Yes, physician's diagnosis (1)-No (0)                0.001398185 0.0513937
## Yes, physician's diagnosis (1)-Yes, own opinion (2) -0.010924046 0.0433217

## 
## Frequency Table:
## 
##                                                        No (0) 
##                              3                            131 
##           Yes, own opinion (2) Yes, physician's diagnosis (1) 
##                             16                             29 
## 
## Proportions:
## 
##                                                        No (0) 
##                     0.01675978                     0.73184358 
##           Yes, own opinion (2) Yes, physician's diagnosis (1) 
##                     0.08938547                     0.16201117 
## 
## Summary Statistics:
##          data[[categorical_var]] data[[continuous_var]].mean
## 1                                                  3.8349754
## 2                         No (0)                   3.6624859
## 3           Yes, own opinion (2)                   3.5650657
## 4 Yes, physician's diagnosis (1)                   3.5349074
##   data[[continuous_var]].sd data[[continuous_var]].n
## 1                 0.1959173                3.0000000
## 2                 0.8118286              131.0000000
## 3                 0.4404682               16.0000000
## 4                 0.7426828               29.0000000
## 
## ANOVA Results:
##                            Df Sum Sq Mean Sq F value Pr(>F)
## Injury.due.to.an.accident   3   0.59  0.1958   0.329  0.804
## Residuals                 175 104.11  0.5949

Missed work

## 
## Frequency Table:
## 
##                          None (5)  Max. 9 days (4)   10-24 days (3) 
##                0               53               70               28 
##   25-99 days (2) 100-354 days (1) 
##               10               18 
## 
## Proportions:
## 
##                          None (5)  Max. 9 days (4)   10-24 days (3) 
##       0.00000000       0.29608939       0.39106145       0.15642458 
##   25-99 days (2) 100-354 days (1) 
##       0.05586592       0.10055866 
## 
## Summary Statistics:
##   data[[categorical_var]] data[[continuous_var]].mean data[[continuous_var]].sd
## 1                None (5)                   3.6989259                 0.6320818
## 2         Max. 9 days (4)                   3.6426311                 0.5270830
## 3          10-24 days (3)                   3.4119511                 0.6045522
## 4          25-99 days (2)                   3.1900433                 0.6595273
## 5        100-354 days (1)                   3.0554353                 0.8719157
##   data[[continuous_var]].n
## 1               53.0000000
## 2               70.0000000
## 3               28.0000000
## 4               10.0000000
## 5               18.0000000
## 
## ANOVA Results:
##                                                                                           Df
## During.the.last.12.months..how.many.whole.days.have.you.been.off.work.because.of.illness   4
## Residuals                                                                                174
##                                                                                          Sum Sq
## During.the.last.12.months..how.many.whole.days.have.you.been.off.work.because.of.illness   7.99
## Residuals                                                                                 66.65
##                                                                                          Mean Sq
## During.the.last.12.months..how.many.whole.days.have.you.been.off.work.because.of.illness  1.9965
## Residuals                                                                                 0.3831
##                                                                                          F value
## During.the.last.12.months..how.many.whole.days.have.you.been.off.work.because.of.illness   5.212
## Residuals                                                                                       
##                                                                                            Pr(>F)
## During.the.last.12.months..how.many.whole.days.have.you.been.off.work.because.of.illness 0.000546
## Residuals                                                                                        
##                                                                                             
## During.the.last.12.months..how.many.whole.days.have.you.been.off.work.because.of.illness ***
## Residuals                                                                                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Post-hoc Pairwise Comparisons (Tukey HSD):
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = as.formula(paste(continuous_var, "~", categorical_var)), data = data)
## 
## $During.the.last.12.months..how.many.whole.days.have.you.been.off.work.because.of.illness
##                                         diff        lwr         upr     p adj
## Max. 9 days (4)-None (5)         -0.05629483 -0.3669433  0.25435362 0.9873170
## 10-24 days (3)-None (5)          -0.28697477 -0.6855673  0.11161775 0.2778167
## 25-99 days (2)-None (5)          -0.50888263 -1.0970974  0.07933215 0.1243668
## 100-354 days (1)-None (5)        -0.64349061 -1.1089251 -0.17805611 0.0017754
## 10-24 days (3)-Max. 9 days (4)   -0.23067995 -0.6121747  0.15081482 0.4571804
## 25-99 days (2)-Max. 9 days (4)   -0.45258780 -1.0293537  0.12417808 0.1987778
## 100-354 days (1)-Max. 9 days (4) -0.58719579 -1.0380744 -0.13631716 0.0038890
## 25-99 days (2)-10-24 days (3)    -0.22190785 -0.8504239  0.40660819 0.8669060
## 100-354 days (1)-10-24 days (3)  -0.35651584 -0.8719429  0.15891125 0.3176446
## 100-354 days (1)-25-99 days (2)  -0.13460798 -0.8075015  0.53828554 0.9816402

## 
## Frequency Table:
## 
##                          None (5)  Max. 9 days (4)   10-24 days (3) 
##                0               53               70               28 
##   25-99 days (2) 100-354 days (1) 
##               10               18 
## 
## Proportions:
## 
##                          None (5)  Max. 9 days (4)   10-24 days (3) 
##       0.00000000       0.29608939       0.39106145       0.15642458 
##   25-99 days (2) 100-354 days (1) 
##       0.05586592       0.10055866 
## 
## Summary Statistics:
##   data[[categorical_var]] data[[continuous_var]].mean data[[continuous_var]].sd
## 1                None (5)                   3.8434739                 0.8121410
## 2         Max. 9 days (4)                   3.6883628                 0.7274560
## 3          10-24 days (3)                   3.4724666                 0.7377717
## 4          25-99 days (2)                   3.2701970                 0.6756515
## 5        100-354 days (1)                   3.2790777                 0.6956572
##   data[[continuous_var]].n
## 1               53.0000000
## 2               70.0000000
## 3               28.0000000
## 4               10.0000000
## 5               18.0000000
## 
## ANOVA Results:
##                                                                                           Df
## During.the.last.12.months..how.many.whole.days.have.you.been.off.work.because.of.illness   4
## Residuals                                                                                174
##                                                                                          Sum Sq
## During.the.last.12.months..how.many.whole.days.have.you.been.off.work.because.of.illness   6.85
## Residuals                                                                                 97.84
##                                                                                          Mean Sq
## During.the.last.12.months..how.many.whole.days.have.you.been.off.work.because.of.illness  1.7133
## Residuals                                                                                 0.5623
##                                                                                          F value
## During.the.last.12.months..how.many.whole.days.have.you.been.off.work.because.of.illness   3.047
## Residuals                                                                                       
##                                                                                          Pr(>F)
## During.the.last.12.months..how.many.whole.days.have.you.been.off.work.because.of.illness 0.0185
## Residuals                                                                                      
##                                                                                           
## During.the.last.12.months..how.many.whole.days.have.you.been.off.work.because.of.illness *
## Residuals                                                                                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Post-hoc Pairwise Comparisons (Tukey HSD):
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = as.formula(paste(continuous_var, "~", categorical_var)), data = data)
## 
## $During.the.last.12.months..how.many.whole.days.have.you.been.off.work.because.of.illness
##                                          diff        lwr           upr
## Max. 9 days (4)-None (5)         -0.155111067 -0.5314946  0.2212724410
## 10-24 days (3)-None (5)          -0.371007280 -0.8539444  0.1119297912
## 25-99 days (2)-None (5)          -0.573276809 -1.2859613  0.1394077103
## 100-354 days (1)-None (5)        -0.564396130 -1.1283193 -0.0004729167
## 10-24 days (3)-Max. 9 days (4)   -0.215896213 -0.6781175  0.2463251219
## 25-99 days (2)-Max. 9 days (4)   -0.418165741 -1.1169787  0.2806472315
## 100-354 days (1)-Max. 9 days (4) -0.409285063 -0.9555723  0.1370021675
## 25-99 days (2)-10-24 days (3)    -0.202269529 -0.9637833  0.5592442538
## 100-354 days (1)-10-24 days (3)  -0.193388850 -0.8178834  0.4311056718
## 100-354 days (1)-25-99 days (2)   0.008880679 -0.8064011  0.8241624804
##                                      p adj
## Max. 9 days (4)-None (5)         0.7872052
## 10-24 days (3)-None (5)          0.2172455
## 25-99 days (2)-None (5)          0.1781669
## 100-354 days (1)-None (5)        0.0496942
## 10-24 days (3)-Max. 9 days (4)   0.6992218
## 25-99 days (2)-Max. 9 days (4)   0.4680205
## 100-354 days (1)-Max. 9 days (4) 0.2399736
## 25-99 days (2)-10-24 days (3)    0.9487248
## 100-354 days (1)-10-24 days (3)  0.9131823
## 100-354 days (1)-25-99 days (2)  0.9999998

Mental Demands

## 
## Frequency Table:
## 
##                   Very poor (1) Rather poor (2)    Moderate (3) Rather good (4) 
##               2               4              15              55              66 
##   Very good (5) 
##              37 
## 
## Proportions:
## 
##                   Very poor (1) Rather poor (2)    Moderate (3) Rather good (4) 
##      0.01117318      0.02234637      0.08379888      0.30726257      0.36871508 
##   Very good (5) 
##      0.20670391 
## 
## Summary Statistics:
##   data[[categorical_var]] data[[continuous_var]].mean data[[continuous_var]].sd
## 1                                          3.76363636                0.05142595
## 2           Very poor (1)                  2.76136364                0.77794584
## 3         Rather poor (2)                  2.95708514                0.57317330
## 4            Moderate (3)                  3.24463162                0.50819453
## 5         Rather good (4)                  3.62027089                0.54308597
## 6           Very good (5)                  4.13887914                0.49963821
##   data[[continuous_var]].n
## 1               2.00000000
## 2               4.00000000
## 3              15.00000000
## 4              55.00000000
## 5              66.00000000
## 6              37.00000000
## 
## ANOVA Results:
##                                                                                             Df
## How.do.you.rate.your.current.work.ability.with.respect.to.the.mental.demands.of.your.work.   5
## Residuals                                                                                  173
##                                                                                            Sum Sq
## How.do.you.rate.your.current.work.ability.with.respect.to.the.mental.demands.of.your.work.  26.12
## Residuals                                                                                   48.52
##                                                                                            Mean Sq
## How.do.you.rate.your.current.work.ability.with.respect.to.the.mental.demands.of.your.work.   5.223
## Residuals                                                                                    0.280
##                                                                                            F value
## How.do.you.rate.your.current.work.ability.with.respect.to.the.mental.demands.of.your.work.   18.62
## Residuals                                                                                         
##                                                                                              Pr(>F)
## How.do.you.rate.your.current.work.ability.with.respect.to.the.mental.demands.of.your.work. 8.79e-15
## Residuals                                                                                          
##                                                                                               
## How.do.you.rate.your.current.work.ability.with.respect.to.the.mental.demands.of.your.work. ***
## Residuals                                                                                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Post-hoc Pairwise Comparisons (Tukey HSD):
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = as.formula(paste(continuous_var, "~", categorical_var)), data = data)
## 
## $How.do.you.rate.your.current.work.ability.with.respect.to.the.mental.demands.of.your.work.
##                                       diff         lwr       upr     p adj
## Very poor (1)-                  -1.0022727 -2.32405609 0.3195106 0.2499754
## Rather poor (2)-                -0.8065512 -1.95548099 0.3423785 0.3335548
## Moderate (3)-                   -0.5190047 -1.61768352 0.5796740 0.7499224
## Rather good (4)-                -0.1433655 -1.23882702 0.9520961 0.9989929
## Very good (5)-                   0.3752428 -0.73277338 1.4832589 0.9249992
## Rather poor (2)-Very poor (1)    0.1957215 -0.66315520 1.0545982 0.9862838
## Moderate (3)-Very poor (1)       0.4832680 -0.30712725 1.2736632 0.4929850
## Rather good (4)-Very poor (1)    0.8589073  0.07299021 1.6448243 0.0232899
## Very good (5)-Very poor (1)      1.3775155  0.57419155 2.1808395 0.0000266
## Moderate (3)-Rather poor (2)     0.2875465 -0.15703533 0.7321283 0.4279287
## Rather good (4)-Rather poor (2)  0.6631858  0.22661508 1.0997564 0.0002953
## Very good (5)-Rather poor (2)    1.1817940  0.71461337 1.6489746 0.0000000
## Rather good (4)-Moderate (3)     0.3756393  0.09698287 0.6542957 0.0019923
## Very good (5)-Moderate (3)       0.8942475  0.56972760 1.2187674 0.0000000
## Very good (5)-Rather good (4)    0.5186082  0.20515308 0.8320634 0.0000572

## 
## Frequency Table:
## 
##                   Very poor (1) Rather poor (2)    Moderate (3) Rather good (4) 
##               2               4              15              55              66 
##   Very good (5) 
##              37 
## 
## Proportions:
## 
##                   Very poor (1) Rather poor (2)    Moderate (3) Rather good (4) 
##      0.01117318      0.02234637      0.08379888      0.30726257      0.36871508 
##   Very good (5) 
##      0.20670391 
## 
## Summary Statistics:
##   data[[categorical_var]] data[[continuous_var]].mean data[[continuous_var]].sd
## 1                                           3.3965517                 0.2194469
## 2           Very poor (1)                   3.2500000                 0.7702222
## 3         Rather poor (2)                   2.9279146                 0.5922612
## 4            Moderate (3)                   3.4100791                 0.6680964
## 5         Rather good (4)                   3.6813980                 0.6904483
## 6           Very good (5)                   4.2325811                 0.7173528
##   data[[continuous_var]].n
## 1                2.0000000
## 2                4.0000000
## 3               15.0000000
## 4               55.0000000
## 5               66.0000000
## 6               37.0000000
## 
## ANOVA Results:
##                                                                                             Df
## How.do.you.rate.your.current.work.ability.with.respect.to.the.mental.demands.of.your.work.   5
## Residuals                                                                                  173
##                                                                                            Sum Sq
## How.do.you.rate.your.current.work.ability.with.respect.to.the.mental.demands.of.your.work.  24.34
## Residuals                                                                                   80.35
##                                                                                            Mean Sq
## How.do.you.rate.your.current.work.ability.with.respect.to.the.mental.demands.of.your.work.   4.869
## Residuals                                                                                    0.464
##                                                                                            F value
## How.do.you.rate.your.current.work.ability.with.respect.to.the.mental.demands.of.your.work.   10.48
## Residuals                                                                                         
##                                                                                              Pr(>F)
## How.do.you.rate.your.current.work.ability.with.respect.to.the.mental.demands.of.your.work. 8.39e-09
## Residuals                                                                                          
##                                                                                               
## How.do.you.rate.your.current.work.ability.with.respect.to.the.mental.demands.of.your.work. ***
## Residuals                                                                                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Post-hoc Pairwise Comparisons (Tukey HSD):
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = as.formula(paste(continuous_var, "~", categorical_var)), data = data)
## 
## $How.do.you.rate.your.current.work.ability.with.respect.to.the.mental.demands.of.your.work.
##                                        diff         lwr       upr     p adj
## Very poor (1)-                  -0.14655172 -1.84751310 1.5544097 0.9998696
## Rather poor (2)-                -0.46863711 -1.94715864 1.0098844 0.9426430
## Moderate (3)-                    0.01352739 -1.40032772 1.4273825 1.0000000
## Rather good (4)-                 0.28484627 -1.12486870 1.6945612 0.9920879
## Very good (5)-                   0.83602938 -0.58984171 2.2619005 0.5404175
## Rather poor (2)-Very poor (1)   -0.32208539 -1.42734677 0.7831760 0.9596158
## Moderate (3)-Very poor (1)       0.16007912 -0.85705562 1.1772139 0.9975561
## Rather good (4)-Very poor (1)    0.43139800 -0.57997389 1.4427699 0.8219620
## Very good (5)-Very poor (1)      0.98258110 -0.05119120 2.0163534 0.0728130
## Moderate (3)-Rather poor (2)     0.48216450 -0.08995383 1.0542828 0.1521648
## Rather good (4)-Rather poor (2)  0.75348338  0.19167433 1.3152924 0.0021421
## Very good (5)-Rather poor (2)    1.30466649  0.70346645 1.9058665 0.0000000
## Rather good (4)-Moderate (3)     0.27131888 -0.08727526 0.6299130 0.2522281
## Very good (5)-Moderate (3)       0.82250199  0.40488753 1.2401164 0.0000008
## Very good (5)-Rather good (4)    0.55118311  0.14780753 0.9545587 0.0016380

Physical Demands

## 
## Frequency Table:
## 
##                   Very poor (1) Rather poor (2)    Moderate (3) Rather good (4) 
##               0               3              11              50              72 
##   Very good (5) 
##              43 
## 
## Proportions:
## 
##                   Very poor (1) Rather poor (2)    Moderate (3) Rather good (4) 
##      0.00000000      0.01675978      0.06145251      0.27932961      0.40223464 
##   Very good (5) 
##      0.24022346 
## 
## Summary Statistics:
##   data[[categorical_var]] data[[continuous_var]].mean data[[continuous_var]].sd
## 1           Very poor (1)                   2.6363636                 0.7925271
## 2         Rather poor (2)                   2.7512790                 0.4440158
## 3            Moderate (3)                   3.2852679                 0.5714570
## 4         Rather good (4)                   3.6161195                 0.4511017
## 5           Very good (5)                   3.9689067                 0.6917212
##   data[[continuous_var]].n
## 1                3.0000000
## 2               11.0000000
## 3               50.0000000
## 4               72.0000000
## 5               43.0000000
## 
## ANOVA Results:
##                                                                                               Df
## How.do.you.rate.your.current.work.ability.with.respect.to.the.physical.demands.of.your.work.   4
## Residuals                                                                                    174
##                                                                                              Sum Sq
## How.do.you.rate.your.current.work.ability.with.respect.to.the.physical.demands.of.your.work.  20.86
## Residuals                                                                                     53.77
##                                                                                              Mean Sq
## How.do.you.rate.your.current.work.ability.with.respect.to.the.physical.demands.of.your.work.   5.216
## Residuals                                                                                      0.309
##                                                                                              F value
## How.do.you.rate.your.current.work.ability.with.respect.to.the.physical.demands.of.your.work.   16.88
## Residuals                                                                                           
##                                                                                                Pr(>F)
## How.do.you.rate.your.current.work.ability.with.respect.to.the.physical.demands.of.your.work. 1.04e-11
## Residuals                                                                                            
##                                                                                                 
## How.do.you.rate.your.current.work.ability.with.respect.to.the.physical.demands.of.your.work. ***
## Residuals                                                                                       
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Post-hoc Pairwise Comparisons (Tukey HSD):
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = as.formula(paste(continuous_var, "~", categorical_var)), data = data)
## 
## $How.do.you.rate.your.current.work.ability.with.respect.to.the.physical.demands.of.your.work.
##                                      diff         lwr       upr     p adj
## Rather poor (2)-Very poor (1)   0.1149154 -0.88321858 1.1130494 0.9977838
## Moderate (3)-Very poor (1)      0.6489043 -0.26200281 1.5598114 0.2882135
## Rather good (4)-Very poor (1)   0.9797559  0.07676048 1.8827513 0.0261050
## Very good (5)-Very poor (1)     1.3325430  0.41744874 2.2476373 0.0008342
## Moderate (3)-Rather poor (2)    0.5339889  0.02364216 1.0443357 0.0353332
## Rather good (4)-Rather poor (2) 0.8648405  0.36875305 1.3609280 0.0000325
## Very good (5)-Rather poor (2)   1.2176277  0.69984428 1.7354110 0.0000000
## Rather good (4)-Moderate (3)    0.3308516  0.04874680 0.6129564 0.0125971
## Very good (5)-Moderate (3)      0.6836387  0.36492261 1.0023549 0.0000002
## Very good (5)-Rather good (4)   0.3527871  0.05744173 0.6481326 0.0104309

## 
## Frequency Table:
## 
##                   Very poor (1) Rather poor (2)    Moderate (3) Rather good (4) 
##               0               3              11              50              72 
##   Very good (5) 
##              43 
## 
## Proportions:
## 
##                   Very poor (1) Rather poor (2)    Moderate (3) Rather good (4) 
##      0.00000000      0.01675978      0.06145251      0.27932961      0.40223464 
##   Very good (5) 
##      0.24022346 
## 
## Summary Statistics:
##   data[[categorical_var]] data[[continuous_var]].mean data[[continuous_var]].sd
## 1           Very poor (1)                   3.4022989                 1.0001982
## 2         Rather poor (2)                   3.0509404                 0.9485929
## 3            Moderate (3)                   3.4405419                 0.6508604
## 4         Rather good (4)                   3.6914959                 0.6407954
## 5           Very good (5)                   3.9363234                 0.8977151
##   data[[continuous_var]].n
## 1                3.0000000
## 2               11.0000000
## 3               50.0000000
## 4               72.0000000
## 5               43.0000000
## 
## ANOVA Results:
##                                                                                               Df
## How.do.you.rate.your.current.work.ability.with.respect.to.the.physical.demands.of.your.work.   4
## Residuals                                                                                    174
##                                                                                              Sum Sq
## How.do.you.rate.your.current.work.ability.with.respect.to.the.physical.demands.of.your.work.   9.94
## Residuals                                                                                     94.76
##                                                                                              Mean Sq
## How.do.you.rate.your.current.work.ability.with.respect.to.the.physical.demands.of.your.work.  2.4848
## Residuals                                                                                     0.5446
##                                                                                              F value
## How.do.you.rate.your.current.work.ability.with.respect.to.the.physical.demands.of.your.work.   4.563
## Residuals                                                                                           
##                                                                                               Pr(>F)
## How.do.you.rate.your.current.work.ability.with.respect.to.the.physical.demands.of.your.work. 0.00158
## Residuals                                                                                           
##                                                                                                
## How.do.you.rate.your.current.work.ability.with.respect.to.the.physical.demands.of.your.work. **
## Residuals                                                                                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Post-hoc Pairwise Comparisons (Tukey HSD):
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = as.formula(paste(continuous_var, "~", categorical_var)), data = data)
## 
## $How.do.you.rate.your.current.work.ability.with.respect.to.the.physical.demands.of.your.work.
##                                        diff         lwr       upr     p adj
## Rather poor (2)-Very poor (1)   -0.35135841 -1.67634901 0.9736322 0.9490204
## Moderate (3)-Very poor (1)       0.03824302 -1.17095675 1.2474428 0.9999868
## Rather good (4)-Very poor (1)    0.28919702 -0.90950022 1.4878943 0.9635652
## Very good (5)-Very poor (1)      0.53402452 -0.68073360 1.7487826 0.7445327
## Moderate (3)-Rather poor (2)     0.38960143 -0.28786740 1.0670703 0.5088343
## Rather good (4)-Rather poor (2)  0.64055544 -0.01798463 1.2990955 0.0608670
## Very good (5)-Rather poor (2)    0.88538293  0.19804223 1.5727236 0.0044489
## Rather good (4)-Moderate (3)     0.25095400 -0.12353099 0.6254390 0.3500086
## Very good (5)-Moderate (3)       0.49578149  0.07269614 0.9188669 0.0127032
## Very good (5)-Rather good (4)    0.24482749 -0.14723401 0.6368890 0.4235680

Ability to continue

## 
## Frequency Table:
## 
##                                  Unlikely (1)        Not Certain (4) 
##                      3                     13                     39 
## Relatively certain (7) 
##                    124 
## 
## Proportions:
## 
##                                  Unlikely (1)        Not Certain (4) 
##             0.01675978             0.07262570             0.21787709 
## Relatively certain (7) 
##             0.69273743 
## 
## Summary Statistics:
##   data[[categorical_var]] data[[continuous_var]].mean data[[continuous_var]].sd
## 1                                           3.4696970                 0.3812044
## 2            Unlikely (1)                   2.9860140                 0.8199444
## 3         Not Certain (4)                   3.1134976                 0.5372107
## 4  Relatively certain (7)                   3.7323102                 0.5676092
##   data[[continuous_var]].n
## 1                3.0000000
## 2               13.0000000
## 3               39.0000000
## 4              124.0000000
## 
## ANOVA Results:
##                                                                                                                              Df
## Do.you.believe..according.to.your.present.state.of.health..that.you.will.be.able.to.do.your.current.job.two.years.from.now.   3
## Residuals                                                                                                                   175
##                                                                                                                             Sum Sq
## Do.you.believe..according.to.your.present.state.of.health..that.you.will.be.able.to.do.your.current.job.two.years.from.now.  15.68
## Residuals                                                                                                                    58.95
##                                                                                                                             Mean Sq
## Do.you.believe..according.to.your.present.state.of.health..that.you.will.be.able.to.do.your.current.job.two.years.from.now.   5.228
## Residuals                                                                                                                     0.337
##                                                                                                                             F value
## Do.you.believe..according.to.your.present.state.of.health..that.you.will.be.able.to.do.your.current.job.two.years.from.now.   15.52
## Residuals                                                                                                                          
##                                                                                                                               Pr(>F)
## Do.you.believe..according.to.your.present.state.of.health..that.you.will.be.able.to.do.your.current.job.two.years.from.now. 5.38e-09
## Residuals                                                                                                                           
##                                                                                                                                
## Do.you.believe..according.to.your.present.state.of.health..that.you.will.be.able.to.do.your.current.job.two.years.from.now. ***
## Residuals                                                                                                                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Post-hoc Pairwise Comparisons (Tukey HSD):
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = as.formula(paste(continuous_var, "~", categorical_var)), data = data)
## 
## $Do.you.believe..according.to.your.present.state.of.health..that.you.will.be.able.to.do.your.current.job.two.years.from.now.
##                                              diff        lwr       upr
## Unlikely (1)-                          -0.4836830 -1.4479853 0.4806193
## Not Certain (4)-                       -0.3561994 -1.2582216 0.5458229
## Relatively certain (7)-                 0.2626133 -0.6170489 1.1422754
## Not Certain (4)-Unlikely (1)            0.1274836 -0.3546675 0.6096348
## Relatively certain (7)-Unlikely (1)     0.7462963  0.3073986 1.1851939
## Relatively certain (7)-Not Certain (4)  0.6188126  0.3424138 0.8952115
##                                            p adj
## Unlikely (1)-                          0.5634847
## Not Certain (4)-                       0.7354462
## Relatively certain (7)-                0.8659511
## Not Certain (4)-Unlikely (1)           0.9023890
## Relatively certain (7)-Unlikely (1)    0.0001051
## Relatively certain (7)-Not Certain (4) 0.0000002

## 
## Frequency Table:
## 
##                                  Unlikely (1)        Not Certain (4) 
##                      3                     13                     39 
## Relatively certain (7) 
##                    124 
## 
## Proportions:
## 
##                                  Unlikely (1)        Not Certain (4) 
##             0.01675978             0.07262570             0.21787709 
## Relatively certain (7) 
##             0.69273743 
## 
## Summary Statistics:
##   data[[categorical_var]] data[[continuous_var]].mean data[[continuous_var]].sd
## 1                                           3.7454844                 0.6964014
## 2            Unlikely (1)                   3.2572944                 0.8335056
## 3         Not Certain (4)                   3.1838870                 0.5953913
## 4  Relatively certain (7)                   3.8152506                 0.7438823
##   data[[continuous_var]].n
## 1                3.0000000
## 2               13.0000000
## 3               39.0000000
## 4              124.0000000
## 
## ANOVA Results:
##                                                                                                                              Df
## Do.you.believe..according.to.your.present.state.of.health..that.you.will.be.able.to.do.your.current.job.two.years.from.now.   3
## Residuals                                                                                                                   175
##                                                                                                                             Sum Sq
## Do.you.believe..according.to.your.present.state.of.health..that.you.will.be.able.to.do.your.current.job.two.years.from.now.  13.86
## Residuals                                                                                                                    90.84
##                                                                                                                             Mean Sq
## Do.you.believe..according.to.your.present.state.of.health..that.you.will.be.able.to.do.your.current.job.two.years.from.now.   4.619
## Residuals                                                                                                                     0.519
##                                                                                                                             F value
## Do.you.believe..according.to.your.present.state.of.health..that.you.will.be.able.to.do.your.current.job.two.years.from.now.   8.898
## Residuals                                                                                                                          
##                                                                                                                               Pr(>F)
## Do.you.believe..according.to.your.present.state.of.health..that.you.will.be.able.to.do.your.current.job.two.years.from.now. 1.61e-05
## Residuals                                                                                                                           
##                                                                                                                                
## Do.you.believe..according.to.your.present.state.of.health..that.you.will.be.able.to.do.your.current.job.two.years.from.now. ***
## Residuals                                                                                                                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Post-hoc Pairwise Comparisons (Tukey HSD):
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = as.formula(paste(continuous_var, "~", categorical_var)), data = data)
## 
## $Do.you.believe..according.to.your.present.state.of.health..that.you.will.be.able.to.do.your.current.job.two.years.from.now.
##                                               diff         lwr       upr
## Unlikely (1)-                          -0.48818997 -1.68520655 0.7088266
## Not Certain (4)-                       -0.56159741 -1.68130389 0.5581091
## Relatively certain (7)-                 0.06976616 -1.02218413 1.1617164
## Not Certain (4)-Unlikely (1)           -0.07340744 -0.67191573 0.5251009
## Relatively certain (7)-Unlikely (1)     0.55795613  0.01313964 1.1027726
## Relatively certain (7)-Not Certain (4)  0.63136356  0.28826165 0.9744655
##                                            p adj
## Unlikely (1)-                          0.7155241
## Not Certain (4)-                       0.5635356
## Relatively certain (7)-                0.9983752
## Not Certain (4)-Unlikely (1)           0.9888229
## Relatively certain (7)-Unlikely (1)    0.0424776
## Relatively certain (7)-Not Certain (4) 0.0000225

Satisfaction

Transport

## 
## Frequency Table:
## 
##     1  2  3  4  5 
##  0 13 16 34 73 43 
## 
## Proportions:
## 
##                     1          2          3          4          5 
## 0.00000000 0.07262570 0.08938547 0.18994413 0.40782123 0.24022346 
## 
## Summary Statistics:
##   data[[categorical_var]] data[[continuous_var]].mean data[[continuous_var]].sd
## 1                       1                   2.9532135                 0.5392171
## 2                       2                   2.8835227                 0.5326706
## 3                       3                   3.2007194                 0.5380363
## 4                       4                   3.6775096                 0.4865633
## 5                       5                   3.9918454                 0.5946084
##   data[[continuous_var]].n
## 1               13.0000000
## 2               16.0000000
## 3               34.0000000
## 4               73.0000000
## 5               43.0000000
## 
## ANOVA Results:
##                         Df Sum Sq Mean Sq F value   Pr(>F)    
## transport_satisfaction   4  25.44   6.361    22.5 5.43e-15 ***
## Residuals              174  49.19   0.283                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Post-hoc Pairwise Comparisons (Tukey HSD):
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = as.formula(paste(continuous_var, "~", categorical_var)), data = data)
## 
## $transport_satisfaction
##            diff         lwr       upr     p adj
## 2-1 -0.06969073 -0.61698094 0.4775995 0.9967187
## 3-1  0.24750593 -0.23045032 0.7254622 0.6108181
## 4-1  0.72429617  0.28306471 1.1655276 0.0001081
## 5-1  1.03863191  0.57471686 1.5025470 0.0000000
## 3-2  0.31719665 -0.12716431 0.7615576 0.2862051
## 4-2  0.79398689  0.38938871 1.1985851 0.0000020
## 5-2  1.10832264  0.67910048 1.5375448 0.0000000
## 4-3  0.47679024  0.17246264 0.7811178 0.0002525
## 5-3  0.79112599  0.45475229 1.1274997 0.0000000
## 5-4  0.31433574  0.03257292 0.5960986 0.0203817

## 
## Frequency Table:
## 
##     1  2  3  4  5 
##  0 13 16 34 73 43 
## 
## Proportions:
## 
##                     1          2          3          4          5 
## 0.00000000 0.07262570 0.08938547 0.18994413 0.40782123 0.24022346 
## 
## Summary Statistics:
##   data[[categorical_var]] data[[continuous_var]].mean data[[continuous_var]].sd
## 1                       1                   3.2058355                 0.6835468
## 2                       2                   3.0072352                 0.6879039
## 3                       3                   3.3803366                 0.6966984
## 4                       4                   3.6967363                 0.6890647
## 5                       5                   4.0990494                 0.7128998
##   data[[continuous_var]].n
## 1               13.0000000
## 2               16.0000000
## 3               34.0000000
## 4               73.0000000
## 5               43.0000000
## 
## ANOVA Results:
##                         Df Sum Sq Mean Sq F value   Pr(>F)    
## transport_satisfaction   4  20.44   5.111   10.55 1.12e-07 ***
## Residuals              174  84.25   0.484                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Post-hoc Pairwise Comparisons (Tukey HSD):
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = as.formula(paste(continuous_var, "~", categorical_var)), data = data)
## 
## $transport_satisfaction
##           diff         lwr       upr     p adj
## 2-1 -0.1986003 -0.91484677 0.5176461 0.9404283
## 3-1  0.1745011 -0.45100704 0.8000092 0.9391564
## 4-1  0.4909008 -0.08654510 1.0683466 0.1363299
## 5-1  0.8932138  0.28608162 1.5003460 0.0007121
## 3-2  0.3731014 -0.20844009 0.9546429 0.3952847
## 4-2  0.6895011  0.15999770 1.2190045 0.0038954
## 5-2  1.0918141  0.53008502 1.6535433 0.0000026
## 4-3  0.3163997 -0.08187817 0.7146775 0.1884044
## 5-3  0.7187127  0.27849572 1.1589298 0.0001198
## 5-4  0.4023131  0.03356607 0.7710601 0.0248780

Access to Health Services

## 
## Frequency Table:
## 
##     1  2  3  4  5 
##  0  7 26 37 82 27 
## 
## Proportions:
## 
##                     1          2          3          4          5 
## 0.00000000 0.03910615 0.14525140 0.20670391 0.45810056 0.15083799 
## 
## Summary Statistics:
##   data[[categorical_var]] data[[continuous_var]].mean data[[continuous_var]].sd
## 1                       1                   2.6883117                 0.4926656
## 2                       2                   3.2119547                 0.7029207
## 3                       3                   3.2184743                 0.4666464
## 4                       4                   3.6657689                 0.5396854
## 5                       5                   4.1279461                 0.5213397
##   data[[continuous_var]].n
## 1                7.0000000
## 2               26.0000000
## 3               37.0000000
## 4               82.0000000
## 5               27.0000000
## 
## ANOVA Results:
##                             Df Sum Sq Mean Sq F value   Pr(>F)    
## health_access_satisfaction   4  22.33   5.583   18.57 9.98e-13 ***
## Residuals                  174  52.31   0.301                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Post-hoc Pairwise Comparisons (Tukey HSD):
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = as.formula(paste(continuous_var, "~", categorical_var)), data = data)
## 
## $health_access_satisfaction
##            diff         lwr       upr     p adj
## 2-1 0.523643024 -0.11993123 1.1672173 0.1690253
## 3-1 0.530162630 -0.09278831 1.1531136 0.1355671
## 4-1 0.977457224  0.38232092 1.5725935 0.0001070
## 5-1 1.439634440  0.79859324 2.0806756 0.0000000
## 3-2 0.006519607 -0.38025694 0.3932961 0.9999989
## 4-2 0.453814200  0.11364447 0.7939839 0.0028658
## 5-2 0.915991416  0.50070592 1.3312769 0.0000001
## 4-3 0.447294594  0.14796953 0.7466197 0.0005566
## 5-3 0.909471809  0.52692496 1.2920187 0.0000000
## 5-4 0.462177216  0.12682450 0.7975299 0.0018550

## 
## Frequency Table:
## 
##     1  2  3  4  5 
##  0  7 26 37 82 27 
## 
## Proportions:
## 
##                     1          2          3          4          5 
## 0.00000000 0.03910615 0.14525140 0.20670391 0.45810056 0.15083799 
## 
## Summary Statistics:
##   data[[categorical_var]] data[[continuous_var]].mean data[[continuous_var]].sd
## 1                       1                   2.6520056                 0.6040808
## 2                       2                   3.3978780                 0.7818531
## 3                       3                   3.4282497                 0.6582597
## 4                       4                   3.6878472                 0.7063236
## 5                       5                   4.2476434                 0.6517962
##   data[[continuous_var]].n
## 1                7.0000000
## 2               26.0000000
## 3               37.0000000
## 4               82.0000000
## 5               27.0000000
## 
## ANOVA Results:
##                             Df Sum Sq Mean Sq F value   Pr(>F)    
## health_access_satisfaction   4  20.17   5.043   10.38 1.46e-07 ***
## Residuals                  174  84.53   0.486                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Post-hoc Pairwise Comparisons (Tukey HSD):
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = as.formula(paste(continuous_var, "~", categorical_var)), data = data)
## 
## $health_access_satisfaction
##           diff         lwr       upr     p adj
## 2-1 0.74587235 -0.07224790 1.5639926 0.0923787
## 3-1 0.77624405 -0.01565957 1.5681477 0.0576868
## 4-1 1.03584152  0.27929624 1.7923868 0.0020294
## 5-1 1.59563774  0.78073754 2.4105379 0.0000022
## 3-2 0.03037169 -0.46130385 0.5220472 0.9998100
## 4-2 0.28996917 -0.14245917 0.7223975 0.3493486
## 5-2 0.84976539  0.32184888 1.3776819 0.0001557
## 4-3 0.25959747 -0.12090857 0.6401035 0.3316225
## 5-3 0.81939370  0.33309499 1.3056924 0.0000651
## 5-4 0.55979622  0.13349134 0.9861011 0.0035080

Living Conditions Satisfaction

## 
## Frequency Table:
## 
##     1  2  3  4  5 
##  0 11  9 33 80 42 
## 
## Proportions:
## 
##                     1          2          3          4          5 
## 0.00000000 0.06285714 0.05142857 0.18857143 0.45714286 0.24000000 
## 
## Summary Statistics:
##   data[[categorical_var]] data[[continuous_var]].mean data[[continuous_var]].sd
## 1                       1                   2.8925620                 0.5761344
## 2                       2                   2.6758057                 0.3808890
## 3                       3                   3.2621671                 0.4539926
## 4                       4                   3.5099564                 0.5478049
## 5                       5                   4.1701711                 0.4681431
##   data[[continuous_var]].n
## 1               11.0000000
## 2                9.0000000
## 3               33.0000000
## 4               80.0000000
## 5               42.0000000
## 
## ANOVA Results:
##                                 Df Sum Sq Mean Sq F value Pr(>F)    
## living_conditions_satisfaction   4  30.63   7.658   29.74 <2e-16 ***
## Residuals                      170  43.77   0.257                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 4 observations deleted due to missingness
## 
## Post-hoc Pairwise Comparisons (Tukey HSD):
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = as.formula(paste(continuous_var, "~", categorical_var)), data = data)
## 
## $living_conditions_satisfaction
##           diff         lwr       upr     p adj
## 2-1 -0.2167563 -0.84558420 0.4120716 0.8765790
## 3-1  0.3696051 -0.11748285 0.8566931 0.2282085
## 4-1  0.6173944  0.16749676 1.0672921 0.0019737
## 5-1  1.2776091  0.80374770 1.7514705 0.0000000
## 3-2  0.5863614  0.06024629 1.1124766 0.0205778
## 4-2  0.8341507  0.34226623 1.3260353 0.0000579
## 5-2  1.4943654  0.98047137 2.0082595 0.0000000
## 4-3  0.2477893 -0.04165944 0.5372380 0.1314332
## 5-3  0.9080040  0.58255462 1.2334533 0.0000000
## 5-4  0.6602147  0.39362417 0.9268052 0.0000000

## 
## Frequency Table:
## 
##     1  2  3  4  5 
##  0 11  9 33 80 42 
## 
## Proportions:
## 
##                     1          2          3          4          5 
## 0.00000000 0.06285714 0.05142857 0.18857143 0.45714286 0.24000000 
## 
## Summary Statistics:
##   data[[categorical_var]] data[[continuous_var]].mean data[[continuous_var]].sd
## 1                       1                   3.2664577                 0.8341822
## 2                       2                   2.9095512                 0.6116287
## 3                       3                   3.3628153                 0.6216988
## 4                       4                   3.5592530                 0.7284168
## 5                       5                   4.2554659                 0.5912257
##   data[[continuous_var]].n
## 1               11.0000000
## 2                9.0000000
## 3               33.0000000
## 4               80.0000000
## 5               42.0000000
## 
## ANOVA Results:
##                                 Df Sum Sq Mean Sq F value   Pr(>F)    
## living_conditions_satisfaction   4  25.30   6.326   13.69 1.07e-09 ***
## Residuals                      170  78.57   0.462                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 4 observations deleted due to missingness
## 
## Post-hoc Pairwise Comparisons (Tukey HSD):
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = as.formula(paste(continuous_var, "~", categorical_var)), data = data)
## 
## $living_conditions_satisfaction
##            diff          lwr       upr     p adj
## 2-1 -0.35690650 -1.199417442 0.4856044 0.7695486
## 3-1  0.09635767 -0.556248502 0.7489638 0.9941793
## 4-1  0.29279531 -0.309982852 0.8955735 0.6670694
## 5-1  0.98900817  0.354123130 1.6238932 0.0002807
## 3-2  0.45326417 -0.251631056 1.1581594 0.3927413
## 4-2  0.64970181 -0.009330796 1.3087344 0.0554054
## 5-2  1.34591467  0.657393445 2.0344359 0.0000023
## 4-3  0.19643764 -0.191369146 0.5842444 0.6306567
## 5-3  0.89265051  0.456609669 1.3286913 0.0000007
## 5-4  0.69621286  0.339031802 1.0533939 0.0000025

Work Ability

## === DESCRIPTIVE STATISTICS ===
## 
## Summary of work_ability :
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.000   7.000   8.000   7.469   9.000  10.000 
## 
## Summary of WHOQOL_Score :
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   2.091   3.091   3.591   3.539   4.000   4.955 
## 
## === CORRELATION ANALYSIS ===
## 
## Pearson's correlation (parametric):
## 
##  Pearson's product-moment correlation
## 
## data:  x and y
## t = 9.8451, df = 177, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.4910122 0.6820134
## sample estimates:
##      cor 
## 0.594845 
## 
## 
## Spearman's correlation (non-parametric):
## 
##  Spearman's rank correlation rho
## 
## data:  x and y
## S = 383560, p-value < 2.2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##       rho 
## 0.5987277 
## 
## 
## === LINEAR REGRESSION ===
## 
## Model Summary:
## 
## Call:
## lm(formula = as.formula(paste(y_var, "~", x_var)), data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.67183 -0.28438 -0.00948  0.37580  1.44940 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   2.00297    0.16081  12.455   <2e-16 ***
## work_ability  0.20563    0.02089   9.845   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.522 on 177 degrees of freedom
## Multiple R-squared:  0.3538, Adjusted R-squared:  0.3502 
## F-statistic: 96.93 on 1 and 177 DF,  p-value: < 2.2e-16
## 
## 
## ANOVA:
## Analysis of Variance Table
## 
## Response: WHOQOL_Score
##               Df Sum Sq Mean Sq F value    Pr(>F)    
## work_ability   1 26.410 26.4098  96.926 < 2.2e-16 ***
## Residuals    177 48.228  0.2725                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## === DESCRIPTIVE STATISTICS ===
## 
## Summary of work_ability :
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.000   7.000   8.000   7.469   9.000  10.000 
## 
## Summary of OHQ_Score :
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.828   3.103   3.552   3.636   4.224   5.414 
## 
## === CORRELATION ANALYSIS ===
## 
## Pearson's correlation (parametric):
## 
##  Pearson's product-moment correlation
## 
## data:  x and y
## t = 4.8648, df = 177, p-value = 2.521e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.2071878 0.4665935
## sample estimates:
##       cor 
## 0.3434239 
## 
## 
## Spearman's correlation (non-parametric):
## 
##  Spearman's rank correlation rho
## 
## data:  x and y
## S = 601911, p-value = 3.361e-07
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##       rho 
## 0.3702933 
## 
## 
## === LINEAR REGRESSION ===
## 
## Model Summary:
## 
## Call:
## lm(formula = as.formula(paste(y_var, "~", x_var)), data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.74511 -0.47459 -0.02632  0.50296  2.12499 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    2.5858     0.2225  11.620  < 2e-16 ***
## work_ability   0.1406     0.0289   4.865 2.52e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7223 on 177 degrees of freedom
## Multiple R-squared:  0.1179, Adjusted R-squared:  0.113 
## F-statistic: 23.67 on 1 and 177 DF,  p-value: 2.521e-06
## 
## 
## ANOVA:
## Analysis of Variance Table
## 
## Response: OHQ_Score
##               Df Sum Sq Mean Sq F value    Pr(>F)    
## work_ability   1 12.348 12.3480  23.667 2.521e-06 ***
## Residuals    177 92.349  0.5217                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Cluster Analysis

## 
## Clustering method used: LCA

## # A tibble: 3 × 6
##   cluster work_ability_mean WHOQOL_mean OHQ_mean work_experience_mean     n
##     <int>             <dbl>       <dbl>    <dbl>                <dbl> <int>
## 1       1              8.72        4.20     4.30                 17.9    61
## 2       2              7.36        3.58     3.63                 19.0    59
## 3       3              6.38        2.78     2.90                 22.4    52
Well-being Cluster Profiles
Cluster N OHQ Mean WHOQOL Mean Work Ability Negative Feelings Percent
Cluster 1: High Well-being 11 4.581542 4.413223 8.636364 1.272727 6.1
Cluster 2: Moderate Well-being 55 3.892259 3.829303 8.181818 2.000000 30.7
Cluster 3: Low Well-being 74 3.620668 3.243117 6.851351 3.364865 41.3
Cluster 4: Mixed Profile 39 3.037009 3.443906 7.307692 3.512821 21.8

Well-being Cluster Profiles by Country
Country Cluster N OHQ Mean WHOQOL Mean Work Ability Negative Feelings Percent
Canada Cluster 1: High Well-being 6 4.860632 4.416667 8.833333 1.166667 7.7
Canada Cluster 2: Moderate Well-being 25 3.890260 3.844121 8.400000 2.000000 32.1
Canada Cluster 3: Low Well-being 31 3.512223 3.116555 6.741936 3.290323 39.7
Canada Cluster 4: Mixed Profile 16 3.063962 3.518669 7.250000 3.437500 20.5
United States Cluster 1: High Well-being 5 4.246634 4.409091 8.400000 1.400000 5.0
United States Cluster 2: Moderate Well-being 30 3.893924 3.816955 8.000000 2.000000 29.7
United States Cluster 3: Low Well-being 43 3.698849 3.334360 6.930233 3.418605 42.6
United States Cluster 4: Mixed Profile 23 3.018259 3.391897 7.347826 3.565217 22.8

Well-being Cluster Profiles by Country with Standard Deviations
Country Cluster N OHQ Mean OHQ SD WHOQOL Mean WHOQOL SD Work Ability Mean Work Ability SD Negative Feelings Mean Negative Feelings SD Percent
Canada Cluster 1: High Well-being 6 4.861 0.592 4.417 0.243 8.833 0.983 1.167 0.408 7.7
Canada Cluster 2: Moderate Well-being 25 3.890 0.561 3.844 0.425 8.400 0.913 2.000 0.000 32.1
Canada Cluster 3: Low Well-being 31 3.512 0.832 3.117 0.664 6.742 2.309 3.290 0.824 39.7
Canada Cluster 4: Mixed Profile 16 3.064 0.522 3.519 0.716 7.250 2.082 3.438 0.727 20.5
United States Cluster 1: High Well-being 5 4.247 0.749 4.409 0.345 8.400 1.517 1.400 0.548 5.0
United States Cluster 2: Moderate Well-being 30 3.894 0.439 3.817 0.330 8.000 0.983 2.000 0.000 29.7
United States Cluster 3: Low Well-being 43 3.699 0.834 3.334 0.686 6.930 2.219 3.419 0.852 42.6
United States Cluster 4: Mixed Profile 23 3.018 0.444 3.392 0.468 7.348 1.584 3.565 0.662 22.8
## ## Main Test Results (ANOVA/Kruskal-Wallis)
Tests of Differences Between Clusters Within Countries
statistic p.value Country Variable Test Effect.Size Effect.Size.Interpretation
11.7758 0.0000 Canada OHQ_Score ANOVA 0.3231 Large
12.0589 0.0000 Canada WHOQOL_Score ANOVA 0.3284 Large
13.9794 0.0029 Canada work_ability Kruskal-Wallis 0.1816 Large
53.7083 0.0000 Canada negative_feelings_frequency Kruskal-Wallis 0.6975 Large
10.0023 0.0000 United States OHQ_Score ANOVA 0.2363 Large
9.6264 0.0000 United States WHOQOL_Score ANOVA 0.2294 Large
5.7209 0.1260 United States work_ability Kruskal-Wallis 0.0572 Small
66.0985 0.0000 United States negative_feelings_frequency Kruskal-Wallis 0.6610 Large
## 
## ## Significant Post-hoc Comparisons
Post-hoc Test Results
Comparison diff lwr upr p adj Country Variable Test Z P.unadj P.adj
Cluster 2: Moderate Well-being-Cluster 1: High Well-being -0.9704 -1.7825 -0.1583 0.0127 Canada OHQ_Score TukeyHSD NA NA NA
Cluster 3: Low Well-being-Cluster 1: High Well-being -1.3484 -2.1452 -0.5517 0.0002 Canada OHQ_Score TukeyHSD NA NA NA
Cluster 4: Mixed Profile-Cluster 1: High Well-being -1.7967 -2.6518 -0.9415 0.0000 Canada OHQ_Score TukeyHSD NA NA NA
Cluster 3: Low Well-being-Cluster 2: Moderate Well-being -0.3780 -0.8582 0.1022 0.1728 Canada OHQ_Score TukeyHSD NA NA NA
Cluster 4: Mixed Profile-Cluster 2: Moderate Well-being -0.8263 -1.3982 -0.2544 0.0017 Canada OHQ_Score TukeyHSD NA NA NA
Cluster 4: Mixed Profile-Cluster 3: Low Well-being -0.4483 -0.9982 0.1016 0.1494 Canada OHQ_Score TukeyHSD NA NA NA
Cluster 2: Moderate Well-being-Cluster 1: High Well-being -0.5725 -1.2745 0.1294 0.1490 Canada WHOQOL_Score TukeyHSD NA NA NA
Cluster 3: Low Well-being-Cluster 1: High Well-being -1.3001 -1.9888 -0.6115 0.0000 Canada WHOQOL_Score TukeyHSD NA NA NA
Cluster 4: Mixed Profile-Cluster 1: High Well-being -0.8980 -1.6372 -0.1588 0.0109 Canada WHOQOL_Score TukeyHSD NA NA NA
Cluster 3: Low Well-being-Cluster 2: Moderate Well-being -0.7276 -1.1426 -0.3125 0.0001 Canada WHOQOL_Score TukeyHSD NA NA NA
Cluster 4: Mixed Profile-Cluster 2: Moderate Well-being -0.3255 -0.8198 0.1689 0.3155 Canada WHOQOL_Score TukeyHSD NA NA NA
Cluster 4: Mixed Profile-Cluster 3: Low Well-being 0.4021 -0.0732 0.8774 0.1263 Canada WHOQOL_Score TukeyHSD NA NA NA
Cluster 1: High Well-being - Cluster 2: Moderate Well-being NA NA NA NA Canada work_ability Dunn’s Post-hoc 0.6657 0.5056 0.6067
Cluster 1: High Well-being - Cluster 3: Low Well-being NA NA NA NA Canada work_ability Dunn’s Post-hoc 2.5799 0.0099 0.0296
Cluster 2: Moderate Well-being - Cluster 3: Low Well-being NA NA NA NA Canada work_ability Dunn’s Post-hoc 3.1547 0.0016 0.0096
Cluster 1: High Well-being - Cluster 4: Mixed Profile NA NA NA NA Canada work_ability Dunn’s Post-hoc 1.9795 0.0478 0.0716
Cluster 2: Moderate Well-being - Cluster 4: Mixed Profile NA NA NA NA Canada work_ability Dunn’s Post-hoc 2.0146 0.0439 0.0879
Cluster 3: Low Well-being - Cluster 4: Mixed Profile NA NA NA NA Canada work_ability Dunn’s Post-hoc -0.6595 0.5095 0.5095
Cluster 1: High Well-being - Cluster 2: Moderate Well-being NA NA NA NA Canada negative_feelings_frequency Dunn’s Post-hoc -1.5353 0.1247 0.1496
Cluster 1: High Well-being - Cluster 3: Low Well-being NA NA NA NA Canada negative_feelings_frequency Dunn’s Post-hoc -4.8345 0.0000 0.0000
Cluster 2: Moderate Well-being - Cluster 3: Low Well-being NA NA NA NA Canada negative_feelings_frequency Dunn’s Post-hoc -5.4248 0.0000 0.0000
Cluster 1: High Well-being - Cluster 4: Mixed Profile NA NA NA NA Canada negative_feelings_frequency Dunn’s Post-hoc -4.8692 0.0000 0.0000
Cluster 2: Moderate Well-being - Cluster 4: Mixed Profile NA NA NA NA Canada negative_feelings_frequency Dunn’s Post-hoc -5.1006 0.0000 0.0000
Cluster 3: Low Well-being - Cluster 4: Mixed Profile NA NA NA NA Canada negative_feelings_frequency Dunn’s Post-hoc -0.5676 0.5703 0.5703
Cluster 2: Moderate Well-being-Cluster 1: High Well-being -0.3527 -1.1775 0.4721 0.6794 United States OHQ_Score TukeyHSD NA NA NA
Cluster 3: Low Well-being-Cluster 1: High Well-being -0.5478 -1.3546 0.2590 0.2916 United States OHQ_Score TukeyHSD NA NA NA
Cluster 4: Mixed Profile-Cluster 1: High Well-being -1.2284 -2.0709 -0.3859 0.0014 United States OHQ_Score TukeyHSD NA NA NA
Cluster 3: Low Well-being-Cluster 2: Moderate Well-being -0.1951 -0.6013 0.2111 0.5933 United States OHQ_Score TukeyHSD NA NA NA
Cluster 4: Mixed Profile-Cluster 2: Moderate Well-being -0.8757 -1.3489 -0.4024 0.0000 United States OHQ_Score TukeyHSD NA NA NA
Cluster 4: Mixed Profile-Cluster 3: Low Well-being -0.6806 -1.1217 -0.2395 0.0006 United States OHQ_Score TukeyHSD NA NA NA
Cluster 2: Moderate Well-being-Cluster 1: High Well-being -0.5921 -1.2731 0.0888 0.1115 United States WHOQOL_Score TukeyHSD NA NA NA
Cluster 3: Low Well-being-Cluster 1: High Well-being -1.0747 -1.7408 -0.4086 0.0003 United States WHOQOL_Score TukeyHSD NA NA NA
Cluster 4: Mixed Profile-Cluster 1: High Well-being -1.0172 -1.7128 -0.3216 0.0013 United States WHOQOL_Score TukeyHSD NA NA NA
Cluster 3: Low Well-being-Cluster 2: Moderate Well-being -0.4826 -0.8180 -0.1472 0.0016 United States WHOQOL_Score TukeyHSD NA NA NA
Cluster 4: Mixed Profile-Cluster 2: Moderate Well-being -0.4251 -0.8158 -0.0343 0.0274 United States WHOQOL_Score TukeyHSD NA NA NA
Cluster 4: Mixed Profile-Cluster 3: Low Well-being 0.0575 -0.3066 0.4217 0.9761 United States WHOQOL_Score TukeyHSD NA NA NA
Cluster 1: High Well-being - Cluster 2: Moderate Well-being NA NA NA NA United States negative_feelings_frequency Dunn’s Post-hoc -0.8896 0.3737 0.4484
Cluster 1: High Well-being - Cluster 3: Low Well-being NA NA NA NA United States negative_feelings_frequency Dunn’s Post-hoc -4.2043 0.0000 0.0000
Cluster 2: Moderate Well-being - Cluster 3: Low Well-being NA NA NA NA United States negative_feelings_frequency Dunn’s Post-hoc -6.5445 0.0000 0.0000
Cluster 1: High Well-being - Cluster 4: Mixed Profile NA NA NA NA United States negative_feelings_frequency Dunn’s Post-hoc -4.4031 0.0000 0.0000
Cluster 2: Moderate Well-being - Cluster 4: Mixed Profile NA NA NA NA United States negative_feelings_frequency Dunn’s Post-hoc -6.2888 0.0000 0.0000
Cluster 3: Low Well-being - Cluster 4: Mixed Profile NA NA NA NA United States negative_feelings_frequency Dunn’s Post-hoc -0.7204 0.4713 0.4713

Cmparing Clusters

Well-being Cluster Profiles by Country (4 Consistent Clusters)
Country Cluster N OHQ Mean WHOQOL Mean Work Ability Negative Feelings Percent
Canada Cluster 1: High Well-being 6 3.316 2.758 2.500 3.167 7.7
Canada Cluster 2: Moderate Well-being 20 2.956 3.415 7.600 3.550 25.6
Canada Cluster 3: Low Well-being 25 3.923 3.890 8.440 2.000 32.1
Canada Cluster 4: Mixed Profile 27 3.972 3.460 7.778 2.741 34.6
United States Cluster 1: High Well-being 10 4.791 4.332 8.800 2.800 9.9
United States Cluster 2: Moderate Well-being 27 4.063 3.918 8.074 1.926 26.7
United States Cluster 3: Low Well-being 46 3.353 3.239 6.609 3.283 45.5
United States Cluster 4: Mixed Profile 18 3.036 3.327 7.722 3.611 17.8
## ## Kruskal-Wallis Test Results by Country
Kruskal-Wallis Tests with Effect Sizes
Country Variable Test Statistic p.value Effect.Size
Canada OHQ_Score Kruskal-Wallis 20.8910 0.0001 0.2713
Canada WHOQOL_Score Kruskal-Wallis 13.4554 0.0037 0.1747
Canada work_ability Kruskal-Wallis 21.4955 0.0001 0.2792
Canada negative_feelings_frequency Kruskal-Wallis 34.3234 0.0000 0.4458
United States OHQ_Score Kruskal-Wallis 57.3687 0.0000 0.5737
United States WHOQOL_Score Kruskal-Wallis 46.2710 0.0000 0.4627
United States work_ability Kruskal-Wallis 16.3862 0.0009 0.1639
United States negative_feelings_frequency Kruskal-Wallis 48.7837 0.0000 0.4878
## 
## ## Significant Post-hoc Comparisons (Dunn's Test)
Dunn’s Post-hoc Test Results
Comparison Z P.unadj P.adj Country Variable Test
Cluster 1: High Well-being - Cluster 2: Moderate Well-being 0.5887 0.5561 0.6673 Canada OHQ_Score Dunn’s Post-hoc
Cluster 1: High Well-being - Cluster 3: Low Well-being -1.9540 0.0507 0.1014 Canada OHQ_Score Dunn’s Post-hoc
Cluster 2: Moderate Well-being - Cluster 3: Low Well-being -3.8744 0.0001 0.0006 Canada OHQ_Score Dunn’s Post-hoc
Cluster 1: High Well-being - Cluster 4: Mixed Profile -1.9232 0.0545 0.0817 Canada OHQ_Score Dunn’s Post-hoc
Cluster 2: Moderate Well-being - Cluster 4: Mixed Profile -3.8710 0.0001 0.0003 Canada OHQ_Score Dunn’s Post-hoc
Cluster 3: Low Well-being - Cluster 4: Mixed Profile 0.0731 0.9417 0.9417 Canada OHQ_Score Dunn’s Post-hoc
Cluster 1: High Well-being - Cluster 2: Moderate Well-being -1.9915 0.0464 0.0557 Canada WHOQOL_Score Dunn’s Post-hoc
Cluster 1: High Well-being - Cluster 3: Low Well-being -3.4208 0.0006 0.0037 Canada WHOQOL_Score Dunn’s Post-hoc
Cluster 2: Moderate Well-being - Cluster 3: Low Well-being -2.0938 0.0363 0.0544 Canada WHOQOL_Score Dunn’s Post-hoc
Cluster 1: High Well-being - Cluster 4: Mixed Profile -2.1399 0.0324 0.0971 Canada WHOQOL_Score Dunn’s Post-hoc
Cluster 2: Moderate Well-being - Cluster 4: Mixed Profile -0.1316 0.8953 0.8953 Canada WHOQOL_Score Dunn’s Post-hoc
Cluster 3: Low Well-being - Cluster 4: Mixed Profile 2.1232 0.0337 0.0675 Canada WHOQOL_Score Dunn’s Post-hoc
Cluster 1: High Well-being - Cluster 2: Moderate Well-being -3.2783 0.0010 0.0021 Canada work_ability Dunn’s Post-hoc
Cluster 1: High Well-being - Cluster 3: Low Well-being -4.6021 0.0000 0.0000 Canada work_ability Dunn’s Post-hoc
Cluster 2: Moderate Well-being - Cluster 3: Low Well-being -1.8872 0.0591 0.0887 Canada work_ability Dunn’s Post-hoc
Cluster 1: High Well-being - Cluster 4: Mixed Profile -3.6067 0.0003 0.0009 Canada work_ability Dunn’s Post-hoc
Cluster 2: Moderate Well-being - Cluster 4: Mixed Profile -0.3452 0.7300 0.7300 Canada work_ability Dunn’s Post-hoc
Cluster 3: Low Well-being - Cluster 4: Mixed Profile 1.6729 0.0944 0.1132 Canada work_ability Dunn’s Post-hoc
Cluster 1: High Well-being - Cluster 2: Moderate Well-being -1.0580 0.2901 0.3481 Canada negative_feelings_frequency Dunn’s Post-hoc
Cluster 1: High Well-being - Cluster 3: Low Well-being 2.7039 0.0069 0.0137 Canada negative_feelings_frequency Dunn’s Post-hoc
Cluster 2: Moderate Well-being - Cluster 3: Low Well-being 5.7389 0.0000 0.0000 Canada negative_feelings_frequency Dunn’s Post-hoc
Cluster 1: High Well-being - Cluster 4: Mixed Profile 0.6444 0.5193 0.5193 Canada negative_feelings_frequency Dunn’s Post-hoc
Cluster 2: Moderate Well-being - Cluster 4: Mixed Profile 2.6550 0.0079 0.0119 Canada negative_feelings_frequency Dunn’s Post-hoc
Cluster 3: Low Well-being - Cluster 4: Mixed Profile -3.3809 0.0007 0.0022 Canada negative_feelings_frequency Dunn’s Post-hoc
Cluster 1: High Well-being - Cluster 2: Moderate Well-being 2.1880 0.0287 0.0344 United States OHQ_Score Dunn’s Post-hoc
Cluster 1: High Well-being - Cluster 3: Low Well-being 5.3409 0.0000 0.0000 United States OHQ_Score Dunn’s Post-hoc
Cluster 2: Moderate Well-being - Cluster 3: Low Well-being 4.3456 0.0000 0.0000 United States OHQ_Score Dunn’s Post-hoc
Cluster 1: High Well-being - Cluster 4: Mixed Profile 6.1887 0.0000 0.0000 United States OHQ_Score Dunn’s Post-hoc
Cluster 2: Moderate Well-being - Cluster 4: Mixed Profile 5.3597 0.0000 0.0000 United States OHQ_Score Dunn’s Post-hoc
Cluster 3: Low Well-being - Cluster 4: Mixed Profile 2.0767 0.0378 0.0378 United States OHQ_Score Dunn’s Post-hoc
Cluster 1: High Well-being - Cluster 2: Moderate Well-being 1.7805 0.0750 0.0900 United States WHOQOL_Score Dunn’s Post-hoc
Cluster 1: High Well-being - Cluster 3: Low Well-being 5.2820 0.0000 0.0000 United States WHOQOL_Score Dunn’s Post-hoc
Cluster 2: Moderate Well-being - Cluster 3: Low Well-being 4.8829 0.0000 0.0000 United States WHOQOL_Score Dunn’s Post-hoc
Cluster 1: High Well-being - Cluster 4: Mixed Profile 4.6169 0.0000 0.0000 United States WHOQOL_Score Dunn’s Post-hoc
Cluster 2: Moderate Well-being - Cluster 4: Mixed Profile 3.8180 0.0001 0.0002 United States WHOQOL_Score Dunn’s Post-hoc
Cluster 3: Low Well-being - Cluster 4: Mixed Profile -0.0792 0.9369 0.9369 United States WHOQOL_Score Dunn’s Post-hoc
Cluster 1: High Well-being - Cluster 2: Moderate Well-being 1.3334 0.1824 0.2189 United States work_ability Dunn’s Post-hoc
Cluster 1: High Well-being - Cluster 3: Low Well-being 3.4377 0.0006 0.0035 United States work_ability Dunn’s Post-hoc
Cluster 2: Moderate Well-being - Cluster 3: Low Well-being 2.9114 0.0036 0.0108 United States work_ability Dunn’s Post-hoc
Cluster 1: High Well-being - Cluster 4: Mixed Profile 1.8245 0.0681 0.1362 United States work_ability Dunn’s Post-hoc
Cluster 2: Moderate Well-being - Cluster 4: Mixed Profile 0.7426 0.4577 0.4577 United States work_ability Dunn’s Post-hoc
Cluster 3: Low Well-being - Cluster 4: Mixed Profile -1.7260 0.0843 0.1265 United States work_ability Dunn’s Post-hoc
Cluster 1: High Well-being - Cluster 2: Moderate Well-being 2.8131 0.0049 0.0098 United States negative_feelings_frequency Dunn’s Post-hoc
Cluster 1: High Well-being - Cluster 3: Low Well-being -1.1276 0.2595 0.2595 United States negative_feelings_frequency Dunn’s Post-hoc
Cluster 2: Moderate Well-being - Cluster 3: Low Well-being -5.9182 0.0000 0.0000 United States negative_feelings_frequency Dunn’s Post-hoc
Cluster 1: High Well-being - Cluster 4: Mixed Profile -2.1245 0.0336 0.0504 United States negative_feelings_frequency Dunn’s Post-hoc
Cluster 2: Moderate Well-being - Cluster 4: Mixed Profile -6.1759 0.0000 0.0000 United States negative_feelings_frequency Dunn’s Post-hoc
Cluster 3: Low Well-being - Cluster 4: Mixed Profile -1.5988 0.1099 0.1318 United States negative_feelings_frequency Dunn’s Post-hoc